02-iris_svm_comparison.ipynb

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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm, datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_meshgrid(x, y, h=.02):\n",
    "    \"\"\"Create a mesh of points to plot in\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    x: data to base x-axis meshgrid on\n",
    "    y: data to base y-axis meshgrid on\n",
    "    h: stepsize for meshgrid, optional\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    xx, yy : ndarray\n",
    "    \"\"\"\n",
    "    x_min, x_max = x.min() - 1, x.max() + 1\n",
    "    y_min, y_max = y.min() - 1, y.max() + 1\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
    "                         np.arange(y_min, y_max, h))\n",
    "    return xx, yy\n",
    "\n",
    "\n",
    "def plot_contours(ax, clf, xx, yy, **params):\n",
    "    \"\"\"Plot the decision boundaries for a classifier.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    ax: matplotlib axes object\n",
    "    clf: a classifier\n",
    "    xx: meshgrid ndarray\n",
    "    yy: meshgrid ndarray\n",
    "    params: dictionary of params to pass to contourf, optional\n",
    "    \"\"\"\n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    out = ax.contourf(xx, yy, Z, **params)\n",
    "    return out\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import some data to play with\n",
    "iris = datasets.load_iris()\n",
    "# Take the first two features. We could avoid this by using a two-dim dataset\n",
    "X = iris.data[:, :2]\n",
    "y = iris.target\n",
    "\n",
    "# see the labels used for training\n",
    "print(\"Labels: {}\".format(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#see the category names in the dataset\n",
    "print(\"Categories: {}\".format(iris.target_names))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we create an instance of SVM and fit out data. We do not scale our\n",
    "# data since we want to plot the support vectors\n",
    "C = 1.0  # SVM regularization parameter\n",
    "models = (svm.SVC(kernel='linear', C=C),\n",
    "          svm.LinearSVC(C=C, max_iter=10000),\n",
    "          svm.SVC(kernel='rbf', gamma=0.7, C=C),\n",
    "          svm.SVC(kernel='poly', degree=3, gamma='auto', C=C))\n",
    "models = (model.fit(X, y) for model in models)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# title for the plots\n",
    "titles = ('SVC with linear kernel',\n",
    "          'LinearSVC (linear kernel)',\n",
    "          'SVC with RBF kernel',\n",
    "          'SVC with polynomial (degree 3) kernel')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set-up 2x2 grid for plotting.\n",
    "fig, sub = plt.subplots(2, 2)\n",
    "plt.subplots_adjust(wspace=0.4, hspace=0.4)\n",
    "\n",
    "X0, X1 = X[:, 0], X[:, 1]\n",
    "xx, yy = make_meshgrid(X0, X1)\n",
    "\n",
    "for model, title, ax in zip(models, titles, sub.flatten()):\n",
    "    plot_contours(ax, model, xx, yy,\n",
    "                  cmap=plt.cm.coolwarm, alpha=0.8)\n",
    "    ax.scatter(X0, X1, c=y, cmap=plt.cm.coolwarm, s=20, edgecolors='k')\n",
    "    ax.set_xlim(xx.min(), xx.max())\n",
    "    ax.set_ylim(yy.min(), yy.max())\n",
    "    ax.set_xlabel('Sepal length')\n",
    "    ax.set_ylabel('Sepal width')\n",
    "    ax.set_xticks(())\n",
    "    ax.set_yticks(())\n",
    "    ax.set_title(title)\n",
    "\n",
    "plt.show()"
   ]
  }
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